Peter Drotár

Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease

OBJECTIVE We present the PaHaW Parkinsons disease handwriting database, consisting of handwriting samples from Parkinsons disease (PD) patients and healthy controls. Our goal is to show that kinematic features and pressure features in handwriting can be used for the differential diagnosis of PD. METHODS AND MATERIAL The database contains records from 37 PD patients and 38 healthy controls performing eight different handwriting tasks. The tasks include drawing an Archimedean spiral, repetitively writing orthographically simple syllables and words, and writing of a sentence. In addition to the conventional kinematic features related to the dynamics of handwriting, we investigated new pressure features based on the pressure exerted on the writing surface. To discriminate between PD patients and healthy subjects, three different classifiers were compared: K-nearest neighbors (K-NN), ensemble AdaBoost classifier, and support vector machines (SVM). RESULTS For predicting PD based on kinematic and pressure features of handwriting, the best performing model was SVM with classification accuracy of Pacc=81.3% (sensitivity Psen=87.4% and specificity of Pspe=80.9%). When evaluated separately, pressure features proved to be relevant for PD diagnosis, yielding Pacc=82.5% compared to Pacc=75.4% using kinematic features. CONCLUSION Experimental results showed that an analysis of kinematic and pressure features during handwriting can help assess subtle characteristics of handwriting and discriminate between PD patients and healthy controls.

Decision Support Framework for Parkinson’s Disease Based on Novel Handwriting Markers

Parkinsons disease (PD) is a neurodegenerative disorder which impairs motor skills, speech, and other functions such as behavior, mood, and cognitive processes. One of the most typical clinical hallmarks of PD is handwriting deterioration, usually the first manifestation of PD. The aim of this study is twofold: (a) to find a subset of handwriting features suitable for identifying subjects with PD and (b) to build a predictive model to efficiently diagnose PD. We collected handwriting samples from 37 medicated PD patients and 38 age- and sex-matched controls. The handwriting samples were collected during seven tasks such as writing a syllable, word, or sentence. Every sample was used to extract the handwriting measures. In addition to conventional kinematic and spatio-temporal handwriting measures, we also computed novel handwriting measures based on entropy, signal energy, and empirical mode decomposition of the handwriting signals. The selected features were fed to the support vector machine classifier with radial Gaussian kernel for automated diagnosis. The accuracy of the classification of PD was as high as 88.13%, with the highest values of sensitivity and specificity equal to 89.47% and 91.89%, respectively. Handwriting may be a valuable marker as a diagnostic and screening tool.

Analysis of in-air movement in handwriting

BACKGROUND AND OBJECTIVE Parkinsons disease (PD) is the second most common neurodegenerative disease affecting significant portion of elderly population. One of the most frequent hallmarks and usually also the first manifestation of PD is deterioration of handwriting characterized by micrographia and changes in kinematics of handwriting. There is no objective quantitative method of clinical diagnosis of PD. It is thought that PD can only be definitively diagnosed at postmortem, which further highlights the complexities of diagnosis. METHODS We exploit the fact that movement during handwriting of a text consists not only from the on-surface movements of the hand, but also from the in-air trajectories performed when the hand moves in the air from one stroke to the next. We used a digitizing tablet to assess both in-air and on-surface kinematic variables during handwriting of a sentence in 37 PD patients on medication and 38 age- and gender-matched healthy controls. RESULTS By applying feature selection algorithms and support vector machine learning methods to separate PD patients from healthy controls, we demonstrated that assessing the in-air/on-surface hand movements led to accurate classifications in 84% and 78% of subjects, respectively. Combining both modalities improved the accuracy by another 1% over the evaluation of in-air features alone and provided medically relevant diagnosis with 85.61% prediction accuracy. CONCLUSIONS Assessment of in-air movements during handwriting has a major impact on disease classification accuracy. This study confirms that handwriting can be used as a marker for PD and can be with advance used in decision support systems for differential diagnosis of PD.

Prediction potential of different handwriting tasks for diagnosis of Parkinson's

One of the most frequent clinical hallmarks of Parkinsons disease (PD) is micrographia. Micrographia in PD is characterized by the decreased letter size and by changes in the kinematic aspects including increased movement time, decreased velocities and accelerations, and increased number of changes in velocity and acceleration. Based on the literature survey we proposed template to acquire handwriting during different tasks. In addition to well established tasks for PD diagnosis such as Archimedean spiral, we designed new tasks to acquire all aspects of micrographia. The database consists of eight different handwriting samples from seventy-five subjects. The presented results shows almost 80% overall classification accuracy.

A new modality for quantitative evaluation of Parkinson's disease: In-air movement

Parkinsons disease (PD) is neurodegenerative disorder with very high prevalence rate occurring mainly among elderly. One of the most typical symptoms of PD is deterioration of handwriting that is usually the first manifestation of Parkinsons disease. In this study, a new modality - in-air trajectory during handwriting - is proposed to efficiently diagnose PD. Experimental results showed that analysis of in-air trajectories is capable of assessing subtle motor abnormalities that are connected with PD. Moreover, conjunction of in-air trajectories with conventional on-surface handwriting allows us to build predictive model with PD classification accuracy over 80%. In total, we compute over 600 handwriting features. Then, we select smaller subset of these features using two feature selection algorithms: Mann-Whitney U-test filter and relief algorithm, and map these feature subsets to binary classification response using support vector machines.

Receiver technique for iterative estimation and cancellation of nonlinear distortion in MIMO SFBC-OFDM systems

Multiple-input multiple-output (MIMO) techniques and orthogonal frequency-division multiplexing (OFDM) can be combined to obtain a promising candidate for next generation wireless communications. However, like a single- input single output (SISO) systems one main disadvantage of MIMO-OFDM is high sensitivity to nonlinear distortion that results in significant loss in power efficiency and performance. In order to improve overall system performance this paper introduces a new iterative method for the detection of nonlinearly distorted symbols received by the receiver of the space-frequency block coded (SFBC) MIMO-OFDM transmission system. The proposed approach is based on the Bussgang theorem application and consists in iterative estimation and accurate cancellation of the nonlinear distortion due to the high power amplifier of the transmitter. The numerical performance evaluation shows that the proposed scheme can achieve significant performance improvement compared to conventional receivers.

Receiver based compensation of nonlinear distortion in MIMO-OFDM

Multiple antenna orthogonal frequency division multiplex (MIMO-OFDM) is transmission technique being employed in the future software defined/cognitive radio solutions. The main disadvantage of using MIMO-OFDM is its high sensitivity to the nonlinear amplification due to the specific characteristic of the high power amplifier (HPA) in the transmitter front-end. This phenomena results in the large overall performance degradation in terms of the bit error rate (BER). In this contribution, we present an iterative receiver scheme to reduce the BER of MIMO-OFDM systems undergoing nonlinear distortion. The technique consist in the iterative estimation and compensation of the nonlinear distortion introduced by transmitters HPA. As it will be shown by means of the numerical performance evaluation, the proposed technique reduces significantly BER of MIMO-OFDM systems.

An experimental comparison of feature selection methods on two-class biomedical datasets

Feature selection is a significant part of many machine learning applications dealing with small-sample and high-dimensional data. Choosing the most important features is an essential step for knowledge discovery in many areas of biomedical informatics. The increased popularity of feature selection methods and their frequent utilisation raise challenging new questions about the interpretability and stability of feature selection techniques. In this study, we compared the behaviour of ten state-of-the-art filter methods for feature selection in terms of their stability, similarity, and influence on prediction performance. All of the experiments were conducted on eight two-class datasets from biomedical areas. While entropy-based feature selection appears to be the most stable, the feature selection techniques yielding the highest prediction performance are minimum redundance maximum relevance method and feature selection based on Bhattacharyya distance. In general, univariate feature selection techniques perform similarly to or even better than more complex multivariate feature selection techniques with high-dimensional datasets. However, with more complex and smaller datasets multivariate methods slightly outperform univariate techniques.

Tax optimization in an agent-based model of real-time spectrum secondary market

The wireless communication industry is an essential sector boosting economic progress worldwide. The structure of the legacy wireless communication market, characterised by static licensing schemes, is moving towards real-time secondary spectrum markets. While the technological body of spectrum trading has been discussed in detail, from an economic perspectives there are still a lot of gaps in understanding how these transactions affect the economy of future communication standards. A challenging aspect of the real-time spectrum market deployment is the implementation of the appropriate tax system that impacts the market structure. With regards to this, we aim to build an agent-based model of the real-time secondary spectrum market in which various taxes including value-added tax, corporate tax, consumption tax and fixed tax, are employed. The relations between selected tax type rates and the hypothetical revenue of the national regulator is established using Laffer curves. The results of the analysis confirm the existence of a tax distortion, i.e. a system deviation from the efficient system functioning affected by the tax introduction. To measure the complexity of the tax strategies and the emergent tax distortion, an original approach based on Euclidean metrics defined over a vector space of the system performance indicators was proposed. This approach was later applied in parallel with the traditional Harberger’s triangle methodology. We found that the constrained optimisation with the tax distortion restrictions provide satisfactory results regarding the stability of the tax distortion measure. Therefore, we propose the application of the most effective corporate tax optimisation complemented by selected additional tax types.

Comparative evaluation of OFDMA and SC-FDMA based transmission systems

New evolving standards of the cellular systems, like e.g. Long Term Evolution (LTE), WiMax and Advanced LTE, consider Orthogonal Frequency Division Multiplex Access (OFDMA) as a mature and suitable solution to cope with the Inter Symbol Interference (ISI) due to the multi-path propagation. However, a major drawback of OFDMA which largely limited its application in the real environment is large envelope fluctuation which results in strong nonlinear distortion due to the nonlinear characteristic of power amplifier (PA). One possible solution to tackle this problem has been introduced by the the 3rd Generation Partnership Project (3GPP) consortium. This solution is based on spreading the base-band modulated signal before the application of OFDMA by using Discrete Fourier Transformation (DFT) that leads eventually to lower envelope fluctuation in comparison with that of OFDMA. The presented method is widely recognized as Single Frequency Division Multiplex (SC-FDMA). This paper aims to provide general comparison of both conventional OFDMA and SC-FDMA and draws important conclusions from this comparison.